This is probably the right time to issue a warning about the error of
making transformations on the dependent variable before doing your
analysis. The classic error that newcomers to statistics commit is to
decide that they want to "make their data normal". The assumptions of
most regression methods is that the *errors* need to have the desired
relationship between means and variance, and not that the dependent
variable be "normal". Many times the apparent non-normality will be
"explained" or "captured" by the regression model. Other methods of
modeling non-linear dependence are also available.
I found Harrell's book "Regression Modeling Strategies" to be an
excellent source for alternatives. My copy of V&R's MASS is only the
second edition but chapters 5 & 6 in that edition on linear models
also had examples of using QQ plots on residuals. Checking that text's
website I see that chapters 6 at least is probably similar. They
include the scripts from their chapters along with the MASS package
(installed as part of the VR bundle). My copy is entitled "ch06.r" and
resides in the scripts subdirectory:
/Library/Frameworks/R.framework/Versions/2.8/Resources/library/MASS/
scripts/ch06.R
--
David Winsemius
On Feb 13, 2009, at 8:11 AM, Jason Rupert wrote:
Thank you very much. Thank you again regarding the suggestion
below. I will give that a shot and I guess I've got my work counted
out for me. I counted 45 different distributions.
Is the best way to get a QQPlot of each, to run through producing a
data set for each distribution and then using the qqplot function to
get a QQplot of the distribution and then compare it with my data
distribution?
As you can tell I am not a trained statistician, so any guidance or
suggested further reading is greatly appreciated.
I guess I am pretty sure my data is not a normal distribution due to
doing some of the empirical "Goodness of Fit" tests and comparing
the QQplot of my data against the QQPlot of a normal distribution
with the same number of points. I guess the next step is to figure
out which distribution my data most closely matches.
Also, I guess I could also fool around and take the log, sqrt, etc.
of my data and see if it will then more closely resemble a normal
distribution.
Thank you again for assisting this novice data analyst who is trying
to gain a better understanding of the techniques using this powerful
software package.
--- On Fri, 2/13/09, Gabor Grothendieck <ggrothendi...@gmail.com>
wrote:
From: Gabor Grothendieck <ggrothendi...@gmail.com>
Subject: Re: [R] Website, book, paper, etc. that shows example plots
of distributions?
To: jasonkrup...@yahoo.com
Cc: R-help@r-project.org
Date: Friday, February 13, 2009, 5:43 AM
You can readily create a dynamic display for using qqplot and
similar functions
in conjunction with either the playwith or TeachingDemos packages.
For example, to investigate the effect of the shape parameter in the
skew
normal distribution on its qqplot relative to the normal distribution:
library(playwith)
library(sn)
playwith(qqnorm(rsn(100, shape = shape)),
parameters = list(shape = seq(-3, 3, .1)))
Now move the slider located at the bottom of the window that
appears and watch the plot change in response to changing
the shape value.
You can find more distributions here:
http://cran.r-project.org/web/views/Distributions.html
On Thu, Feb 12, 2009 at 1:04 PM, Jason Rupert <jasonkrup...@yahoo.com>
wrote:
By any chance is any one aware of a website, book, paper, etc. or
combinations of those sources that show plots of different
distributions?
After reading a pretty good whitepaper I became aware of the
benefit of I
the benefit of doing Q-Q plots and histograms to help assess a
distribution.
The whitepaper is called:
"Univariate Analysis and Normality Test Using SAS, Stata, and
SPSS*" , (c) 2002-2008 The Trustees of Indiana University Univariate
Analysis and Normality Test: 1, Hun Myoung Park
Unfortunately the white paper does not provide an extensive amount of
example distributions plotted using Q-Q plots and histograms, so I
am curious if
there is a "portfolio"-type website or other whitepaper shows
examples of various types of distributions.
It would be helpful to see a bunch of Q-Q plots and their associated
histograms to get an idea of how the distribution looks in
comparison against
the Gaussian.
I think seeing the plot really helps.
Thank you for any insights.
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